精英反向学习及柯西扰动引导的瞪羚优化算法

Elite reverse learning and Cauchy perturbation-guided gazelle optimization algorithm

针对瞪羚优化算法收敛精度低和易陷入局部最优的问题,提出一种精英反向学习及柯西扰动引导的瞪羚优化算法(improved gazelle optimization algorithm,IGOA)。首先,对瞪羚个体利用精英反向学习策略进行初始化,提升初始解的质量并增加种群多样性;其次,在算法迭代初期,利用二阶段非线性惯性权重引导种群的位置更新方式,提高算法的精度并均衡算法的全局搜索和局部搜索;最后,将存活率引导的柯西扰动策略引入勘探阶段种群的位置更新公式中,提升算法跳出局部最优的能力。利用12个基准测试函数和Wilcoxon秩和检验在8个对比算法上进行实验检测,结果表明改进算法寻优精度更高、收敛速度更快且具有跳出局部最优的能力。在齿轮系和三杆桁架设计两个实际工程问题上验证了IGOA的实用性和有效性。

Aiming at the problems of low convergence accuracy of gazelle optimization algorithm and easy to fall into local optimum,an elite inverse learning and Cauchy perturbation-guided improved gazelle optimization algorithm(IGOA)is proposed.Firstly,the gazelle individuals are initialized using an elite reverse learning strategy to improve the quality of the initial solution and increase the population diversity.Secondly,at the beginning of the algorithm iteration,the twostage nonlinear inertia weights are used to guide the position updating method of the population,which improves the accuracy of the algorithm and balances the global and local searches of the algorithm.Finally,the survival rate-guided Cauchy perturbation strategy is introduced into the position updating formula of the population in the exploration stage to improve the ability of the algorithm to jump out of the local optimum.Experimental tests are carried out on 8 comparison algorithms using 12 benchmark test functions and Wilcoxon rank sum test,and the results show that the improved algorithm has higher optimization accuracy,faster convergence speed and ability to jump out of local optimum.The practicality and effectiveness of IGOA are verified on two real engineering problems,namely,gear train and three-bar truss design.